The transformative power of AI agents is unlocked by professionals with deep domain knowledge who can craft highly specific, iterative prompts and integrate the agent into a valid workflow. The technology itself does not compensate for a lack of expertise or flawed underlying processes.

The users who gain the most from AI tools are either deep domain experts who can guide the AI with precision or complete novices unhampered by previous knowledge. Those with intermediate-level skills often get stuck, as they lack the expertise to direct the AI effectively or the naivety to experiment freely.

While AI-native, new graduates often lack the business experience and strategic context to effectively manage AI tools. Companies will instead prioritize senior leaders with high AI literacy who can achieve massive productivity gains, creating a challenging job market for recent graduates and a leaner organizational structure.

Contrary to the belief that AI levels the playing field, senior engineers extract more value from it. They leverage their experience to guide the AI, critically review its output as they would a junior hire's code, and correct its mistakes. This allows them to accelerate their workflow without blindly shipping low-quality code.

If AI were perfect, it would simply replace tasks. Because it is imperfect and requires nuanced interaction, it creates demand for skilled professionals who can prompt, verify, and creatively apply it. This turns AI's limitations into a tool that requires and rewards human proficiency.

By replacing junior roles, AI eliminates the primary training ground for the next generation of experts. This creates a paradox: the very models that need expert data to improve are simultaneously destroying the mechanism that produces those experts, creating a future data bottleneck.

AI accelerates data retrieval, but it creates a dangerous knowledge gap. Junior employees can find facts (e.g., in a financial statement) without the experience-based judgment to understand their deeper connections and second-order consequences for the business.

GSB professors warn that professionals who merely use AI as a black box—passing queries and returning outputs—risk minimizing their own role. To remain valuable, leaders must understand the underlying models and assumptions to properly evaluate AI-generated solutions and maintain control of the decision-making process.

As AI agents handle tasks previously done by junior staff, companies struggle to define entry-level roles. This creates a long-term problem: without a training ground for junior talent, companies will face a severe shortage of experienced future leaders.